Bio-Inspired Optimization: A hearing-based metaheuristic Algorithm for Global Optimization Problems

Abstract A new bio-inspired metaheuristic optimization technique called the Hearing Algorithm (HA) which emulates the mechanistic principles of the human auditory system is presented in this study. The algorithm draws inspiration from the processes of sound localization, echo propagation, selective attention, and signal processing, to formulate a mathematical optimization framework that effectively balances explo- ration and exploitation capabilities. The algorithm core mechanisms include solution attraction toward optimal regions (sound localization) and stochastic perturbation for search space exploration (echo effect), governed by learning rate and noise level parameters. A theoretical convergence theorem for the algorithm under specific parameter adaptation conditions was also presented. Experimental validation on op- timization benchmark functions including Rosenbrock, Griewank, Rastrigin, Ackley, Powell, and Sphere demonstrates the algorithm’s efficacy across varying dimensional- ity (1-30D). Comparative analysis against some established metaheuristics algorithm, Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Differential Evolution (DE), Firefly Algorithm (FA), and Flower Pollination Algorithm (FPA), reveals that the proposed Hearing Algorithm exhibits superior exploitation capabilities for certain unimodal functions while maintaining competitive performance on multimodal land- scapes. Statistical analysis of algorithm performance across multiple independent runs supports the robustness of these findings. The results indicate that auditory- inspired optimization mechanisms offer promising avenues for addressing continuous global optimization problems, particularly those characterized by unimodal or mod- erately multimodal objective functions.